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首页> 外文会议>European Conference on Computer Vision(ECCV 2004) pt.4; 20040511-20040514; Prague; CZ >An MCMC-Based Particle Filter for Tracking Multiple Interacting Targets
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An MCMC-Based Particle Filter for Tracking Multiple Interacting Targets

机译：基于MCMC的粒子过滤器，用于跟踪多个交互目标

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摘要

We describe a Markov chain Monte Carlo based particle filter that effectively deals with interacting targets, i.e., targets that are influenced by the proximity and/or behavior of other targets. Such interactions cause problems for traditional approaches to the data association problem. In response, we developed a joint tracker that includes a more sophisticated motion model to maintain the identity of targets throughout an interaction, drastically reducing tracker failures. The paper presents two main contributions: (1) we show how a Markov random field (MRF) motion prior, built on the fly at each time step, can substantially improve tracking when targets interact, and (2) we show how this can be done efficiently using Markov chain Monte Carlo (MCMC) sampling. We prove that incorporating an MRF to model interactions is equivalent to adding an additional interaction factor to the importance weights in a joint particle filter. Since a joint particle filter suffers from exponential complexity in the number of tracked targets, we replace the traditional importance sampling step in the particle filter with an MCMC sampling step. The resulting filter deals efficiently and effectively with complicated interactions when targets approach each other. We present both qualitative and quantitative results to substantiate the claims made in the paper, including a large scale experiment on a video-sequence of over 10,000 frames in length.
机译：我们描述了基于马尔可夫链蒙特卡罗的粒子滤波器，该粒子滤波器有效地处理了相互作用的目标，即受其他目标的接近性和/或行为影响的目标。这种交互给传统的数据关联问题方法带来了问题。作为响应，我们开发了一种联合跟踪器，其中包括一个更复杂的运动模型，可以在整个交互过程中保持目标的身份，从而大大减少跟踪器的故障。本文提出了两个主要的贡献：（1）我们展示了在每个时间步上动态建立的先验马尔可夫随机场（MRF）运动如何在目标交互时显着改善跟踪；以及（2）我们展示了如何做到这一点使用马尔可夫链蒙特卡洛（MCMC）采样有效地完成了。我们证明，将MRF纳入模型交互作用等同于在联合粒子过滤器中为重要性权重添加一个附加的交互因子。由于联合粒子滤波器在跟踪目标的数量上遭受指数复杂性的困扰，因此我们将粒子滤波器中的传统重要性采样步骤替换为MCMC采样步骤。当目标彼此接近时，生成的过滤器可以有效地处理复杂的交互。我们提供定性和定量结果，以证实本文提出的主张，包括对长度超过10,000帧的视频序列进行大规模实验。

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